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1.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-325911

ABSTRACT

Pharmaceutical and non-pharmaceutical interventions (NPIs) have been crucial for controlling COVID-19. This is complemented by voluntary preventive behaviour, thereby building a complex interplay between risk perception, behaviour, and disease spread. We studied how voluntary health-protective behaviour and vaccination willingness impact the long-term dynamics combining COVID-19 data and modelling. We analysed how different levels of mandatory NPIs determine how individuals use their leeway for voluntary actions. If mandatory NPIs are too weak, COVID-19 incidence will surge, implying high morbidity and mortality before individuals can act;if they are too strong, one expects a rebound wave once restrictions are lifted, challenging the transition to endemicity. Conversely, with moderate mandatory NPIs, individuals effectively adapt their behaviour following their risk perception, mitigating disease spread effectively. Furthermore, together with high vaccination rates, these scenarios offer a robust way to mitigate the impacts of the Omicron Variant of Concern. Altogether, our work highlights the importance of appropriate mandatory NPIs to maximise the impact of individual voluntary actions in pandemic control.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317552

ABSTRACT

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of disease import, of changed activity participation rates over time (coming from mobility data), of masks, of indoors vs.\ outdoors leisure activities, and of contact tracing. Results show that the model is able to credibly track the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model clearly shows the effects of contact reductions, school closures/vacations, or the effect of moving leisure activities from outdoors to indoors in fall. Sensitivity tests show that all ingredients of the model are necessary to track the current infection dynamics. One interesting result from the mobility data is that behavioral changes of the population mostly happened \textit{before} the government-initiated so-called contact ban came into effect. Similarly, people started drifting back to their normal activity patterns \emph{before} the government officially reduced the contact ban. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, consequences of wearing masks in certain situations, or contact tracing.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-295758

ABSTRACT

In this position paper, a large group of interdisciplinary experts outlines response strategies against the spread of SARS-CoV-2 in the winter of 2021/2022 in Germany. We review the current state of the COVID-19 pandemic, from incidence and vaccination efficacy to hospital capacity. Building on this situation assessment, we illustrate various possible scenarios for the winter, and detail the mechanisms and effectiveness of the non-pharmaceutical interventions, vaccination, and booster. With this assessment, we want to provide orientation for decision makers about the progress and mitigation of COVID-19.

4.
PLoS One ; 16(10): e0259037, 2021.
Article in English | MEDLINE | ID: covidwho-1496524

ABSTRACT

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Contact Tracing/methods , Berlin , COVID-19/metabolism , Cell Phone/trends , Computer Simulation , Germany , Hand Disinfection/trends , Humans , Masks/trends , Models, Theoretical , Physical Distancing , Population Dynamics/trends , SARS-CoV-2/pathogenicity , Systems Analysis
5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-291353

ABSTRACT

With winter coming in the northern hemisphere, disadvantageous seasonality of SARS-CoV-2 requires high immunity levels in the population or increasing non-pharmaceutical interventions (NPIs), compared to summer. Otherwise intensive care units (ICUs) might fill up. However, compliance with mandatory NPIs, vaccine uptake, and individual protective measures depend on individuals' opinions and behavior. Opinions, in turn, depend on information, e.g., about vaccine safety or current infection levels. Therefore, understanding how information about the pandemic affects its spread through the modulation of voluntary protection-seeking behaviors is crucial for better preparedness this winter and for future crises.

6.
Physica A: Statistical Mechanics and its Applications ; : 126322, 2021.
Article in English | ScienceDirect | ID: covidwho-1351808

ABSTRACT

We present an agent-based epidemiological model that is based on an agent-based model for traffic and mobility. The model consists of individual agents that follow individual daily activity plans, which include, for each activity, locations, start times, and end times. Evidently, one can place a virus spreading dynamic on top of this, by infecting one or more agents, and then track the resulting virus dynamics through the model. Normally, the model is used to investigate non-pharmaceutical interventions. In the present paper, we undertake steps to better understand the infection graph. It becomes clear that the typical infection graph representation that connects individual people is an even more expensive representation than our original, already expensive data-driven mobility model. We then undertake first steps towards analysing the model with respect to a possible percolation transition.

7.
PLoS One ; 16(4): e0249676, 2021.
Article in English | MEDLINE | ID: covidwho-1197376

ABSTRACT

The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. As long as effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, self-isolation and quarantine as well as far-reaching shutdowns of economic activity and public life are the only available strategies to prevent the virus from spreading. These interventions must meet conflicting requirements where some objectives, like the minimization of disease-related deaths or the impact on health systems, demand for stronger counter-measures, while others, such as social and economic costs, call for weaker counter-measures. Therefore, finding the optimal compromise of counter-measures requires the solution of a multi-objective optimization problem that is based on accurate prediction of future infection spreading for all combinations of counter-measures under consideration. We present a strategy for construction and solution of such a multi-objective optimization problem with real-world applicability. The strategy is based on a micro-model allowing for accurate prediction via a realistic combination of person-centric data-driven human mobility and behavior, stochastic infection models and disease progression models including micro-level inclusion of governmental intervention strategies. For this micro-model, a surrogate macro-model is constructed and validated that is much less computationally expensive and can therefore be used in the core of a numerical solver for the multi-objective optimization problem. The resulting set of optimal compromises between counter-measures (Pareto front) is discussed and its meaning for policy decisions is outlined.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Berlin/epidemiology , COVID-19/epidemiology , Communicable Disease Control , Computer Simulation , Humans , Models, Statistical , SARS-CoV-2/isolation & purification , Stochastic Processes
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